Representationalism—the view that scientific modeling is best understood in representational terms—is the received view in contemporary philosophy of science. Contributions to this literature have focused on a number of puzzles concerning the nature of representation and the epistemic role of misrepresentation, without considering whether these puzzles are the product of an inadequate analytical framework. The goal of this paper is to suggest that this possibility should be taken seriously. The argument has two parts, employing the “can’t have” and “don’t need” tactics drawn from philosophy of mind. On the one hand, I propose that representationalism doesn’t work: different ways to flesh out representationalism create a tension between its ontological and epistemological components and thereby undermine the view. On the other hand, I propose that representationalism is not needed in the first place—a position I articulate based on a pragmatic stance on the success of scientific research and on the feasibility of alternative philosophical frameworks. I conclude that representationalism is untenable and unnecessary, a philosophical dead end. A new way of thinking is called for if we are to make progress in our understanding of scientific modeling.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Batterman, R. W., & Rice, C. C. (2014). Minimal model explanations. Philosophy of Science, 81(3), 349–376.
Bokulich, A. (2012). Distinguishing explanatory from nonexplanatory fictions. Philosophy of Science, 79(5), 725–737.
Bokulich, A. (2017). Models and explanation. In Springer handbook of model-based science (pp. 103–118). Springer.
Callender, C., & Cohen, J. (2006). There is no special problem about scientific representation. Theoria Revista de Teoría, Historia y Fundamentos de la Ciencia, 21(1), 67–85.
Chakravartty, A. (2010). Informational versus functional theories of scientific representation. Synthese, 172(2), 197–213.
Contessa, G. (2007). Scientific representation, interpretation, and surrogative reasoning. Philosophy of Science, 74(1), 48–68.
Elgin, C. Z. (2004). True enough. Philosophical Issues, 14(1), 113–131.
Elgin, C. Z. (2017). True enough. Cambridge: MIT Press.
Frigg, R., & Nguyen. J. (2017a). Models and representation. In Springer handbook of model-based science (pp. 49–102). Springer.
Frigg, R., & Nguyen, J. (2017b). Scientific representation is representation-as. In H. K. Chao & J. Reiss (Eds.), Philosophy of science in practice (pp. 149–179). Berlin: Springer.
Gelfert, A. (2017). The ontology of models. In Springer handbook of model-based science (pp. 5–23). Springer.
Giere, R. (2010). An agent-based conception of models and scientific representation. Synthese, 172(2), 269–281.
Giere, R. N. (1988). Explaining science: A cognitive approach. Chicago: University of Chicago Press.
Giere, R. N. (2004). How models are used to represent reality. Philosophy of Science, 71(5), 742–752.
Giere, R. N. (2006). Scientific perspectivism. Chicago: University of Chicago Press.
Godfrey-Smith, P. (2006a). The strategy of model-based science. Biology and Philosophy, 21, 725–740.
Godfrey-Smith, P. (2006b). Theories and models in metaphysics. The Harvard Review of Philosophy, 14(1), 4–19.
Hughes, R. I. (1997). Models and representation. Philosophy of Science, 64, S325–S336.
Hutto, D., & Myin, E. (2013). Radical enactivism: Basic minds without content. Cambridge, MA: MIT Press.
Isaac, A. M. (2013). Modeling without representation. Synthese, 190, 3611–3623.
James, W. (1907). Pragmatism, a new name for some old ways of thinking: Popular lectures on philosophy. Harlow: Longmans, Green and Co.
Kennedy, A. G. (2012). A non representationalist view of model explanation. Studies in History and Philosophy of Science, 43, 326–332.
Knuuttila, T. (2010). Not just underlying structures: Towards a semiotic approach to scientific representation and modeling. In Bergman, M., Paavola, A.P.S., & Rydenfelt, H. (Eds.) Ideas in action: Proceedings of the applying peirce conference (pp. 163–172).
Knuuttila, T. (2011). Modelling and representing: An artefactual approach to model-based representation. Studies in History and Philosophy of Science Part A, 42(2), 262–271.
Lloyd, E. A. (2010). Conrmation and robustness of climate models. Philosophy of Science, 77(5), 971–984.
MacBride, F. (2016). Relations. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy, Winter 2016 edition. Stanford: Metaphysics Research Lab, Stanford University.
Morgan, M., & Morrison, M. (1999). Models as mediators: Perspectives on natural and social science. Ideas in context. Cambridge: Cambridge University Press.
Morrison, M. (2015). Reconstructing reality: Models, mathematics, and simulations. Oxford: Oxford University Press.
Myin, E., & Hutto, D. D. (2015). Rec: Just radical enough. Studies in Logic, Grammar and Rhetoric, 41(1), 61–71.
Parker, W. S. (2011). When climate models agree: The significance of robust model predictions. Philosophy of Science, 78(4), 579–600.
Pincock, C. (2012). Mathematics and scientific representation. Oxford: Oxford University Press.
Potochnik, A. (2015). The diverse aims of science. Studies in History and Philosophy of Science Part A, 53, 71–80.
Potochnik, A. (2017). Idealization and the aims of science. Chicago: The University Chicago Press.
Suarez, M. (2003). Scientific representation: Against similarity and isomorphism. International Studies in the Philosophy of Science, 17(3), 225–244.
Suarez, M. (2004). An inferential conception of scientific representation. Philosophy of Science, 71(5), 767–779.
van Fraassen, B. C. (1980). The scientific image. Oxford: Clarendon Press.
van Fraassen, B. C. (2008). Scientific representation: Paradoxes of perspective. Oxford: Oxford University Press.
Weisberg, M. (2012). Simulation and similarity: Using models to understand the world. Oxford: Oxford University Press.
Wimsatt, W. C. (1987). False models as means to truer theories. In N. Nitecki & A. Hoffman (Eds.), Neutral models in biology (pp. 23–55). Oxford: Oxford University Press.
I have presented ideas related to this paper at various conferences over the past couple of years, and I have benefited from questions and objections raised by more people than I can hope to name. I am grateful for all of these interactions and recognize the crucial role they have played in helping me develop my thinking. Very special thanks go to Angela Potochnik for her extensive and insightful comments on multiple drafts of this paper. My research was supported by a dissertation fellowship from the Charles Phelps Taft Research Center at the University of Cincinnati.
About this article
Cite this article
de Oliveira, G.S. Representationalism is a dead end. Synthese 198, 209–235 (2021). https://doi.org/10.1007/s11229-018-01995-9
- Scientific modeling
- Epistemology of science